• Build a transparent end-to-end process for handling requests
• Switch from manual task allocation to automatic routing across all communication channels, including mail, instant messengers, and online booking tools
• Implement automation tools such as macros, scripts, templates, and auto-responses to reduce routine and offload the first line of support.
• Formation of operational metrics and building dashboards to monitor team performance. Monitor the indicators of reaction speed, decision time, quality and productivity
• Implementation of new functions in the back-office system to increase employee efficiency - develop AI-native approaches and automation
• Calculate the required number of employees, taking into account seasonality, workload and SLA

• Researches
• Conduct qualitative research: in-depth interviews, usability tests, diary studies, field research.
• Launch quantitative research: surveys, MaxDiff, Kano, NPS/CSAT.
• Link user insights with product analytics and business metrics.
• Conduct a competitive analysis of the travel market and related digital products.
• Segment the audience and find insights on different types of users.
• ResearchOps
• Build research processes and a research repository from scratch.
• Implement tools, templates, a database of respondents, and research standards.
• To help teams use research in discovery processes.
• Work with stakeholders and implement insights into product solutions.
• AI-first approach
• Use GPT, Claude, Gemini, and other AI tools to analyze interviews, process feedback, and synthesize insights.
• Create and improve AI pipelines for research tasks.
• Validate the results and critically evaluate the conclusions of AI.

• Proficiency in Excel, including basic functions and formulas for data processing and analysis;
• At least 1 year of experience as an accountant, reconciliation specialist, or finance department employee;
• Experience working with large amounts of information and data arrays;
• High attention to detail, accuracy in working with numbers;
• Responsibility, a systematic approach to completing tasks and the ability to independently organize the workflow;
• Work experience in the field of travel will be an advantage.

• Maintaining correct and polite communication in written and voice channels, the ability to work with clients
• Strong command of GDS (Amadeus, Sabre, Hitit, etc.)
• Knowledge of airline ADM policies, BSP rules
• Kazakh and Russian languages proficiency; knowledge of English will be an advantage
• Confident use of computers and office software

• At least 3 years of experience in Machine Learning / Data Science / Research Engineering.
• Confident ownership:Python,SQL.
  • Python,
  • SQL.
• Strong mathematical base:probability theory, mathematical statistics, linear algebra, optimization methods.
  • probability theory,
  • mathematical statistics,
  • linear algebra,
  • optimization methods.
• Good understanding of:machine learning fundamentals,statistical modeling,experimentation,optimization methods.
  • machine learning fundamentals,
  • statistical modeling,
  • experimentation,
  • optimization methods.
• Practical experience in several ML areas: recommendation/retrieval/ranking systems, forecasting and time series,predictive modeling, experiment and causal inference, deep learning.
  • recommendation/retrieval/ranking systems,
  • forecasting and time series,
  • predictive modeling,
  • experimentation and causal inference,
  • deep learning.
• Experience working with production ML systems:training and deployment of models,inference pipelines,monitoring/evaluation,reproducible experiment workflows.
  • training and deployment of models,
  • inference pipelines,
  • monitoring/evaluation,
  • reproducible experimentation workflows.
• Understanding:offline/online evaluation,model generalization,data leakage,retrieval vs ranking architectures,causal vs predictive modeling.
  • offline/online evaluation,
  • model generalization,
  • data leakage,
  • retrieval vs ranking architectures,
  • causal vs predictive modeling.
• Experience working with modern ML tooling:PyTorch/JAX,MLflow or analogues,orchestration/workflow tools,Docker,Git.
  • PyTorch/JAX,
  • MLflow or analogues,
  • orchestration/workflow tools,
  • Docker,
  • Git.
• The ability to independently conduct research: read and understand papers, compare approaches, design experiments, adapt research solutions to real product tasks.
  • read and understand papers,
  • compare approaches,
  • design experiments,
  • adapt research solutions to meet real-world product challenges.
• That would be a plus:experience in recommendation systems / ranking / pricing / forecasting, experience working in product-oriented ML teams, experience working with high-scale data environments.
  • experience in recommendation systems / ranking / pricing / forecasting,
  • work experience in product-oriented ML teams,
  • experience working with high-scale data environments.

• To hold meetings: diagnosis of Client's pains / tasks, demo, project protection.
• To get acquainted with the Clients' business, analyze data, form hypotheses on how our products can solve the Client's problem, and adapt them to the Client's tasks and characteristics.
• Formulate IT solutions to the Client's tasks, prepare commercial proposals, form the project budget, structure and timing of work, calculate the result from implementation, observing the "price / value“ parity.
• Develop technical specifications based on Customer requests (analysis of data sources: documents and processes of accounting systems, current reports for implementing Customer requirements), monitor development, and support Customer users at the trial stage.
• Record expert videos, create articles

• At least 1 year of experience in Internet marketing;
• Practical experience of setting up and maintaining advertising campaigns in Yandex Direct and Google Ads;
• Understanding key marketing metrics;
• Good written and oral communication skills;
• Knowledge of English at least B2;
• Business communication and negotiation skills;
• Responsibility, independence and result orientation.

It will be an advantage:

• Work experience in B2B sales;
• Work experience in IT companies or with IT products;
• Understanding the API, CRM, and integrations will be a plus.

Requires experience/understanding in working with :

• Configuring and administering Elastics
• K8s
• Zabbix
• Jenkins, GitLab CI/CD
• VPN
• Setting up networks and routes
• 3Proxy, HAProxy
• Linux, Windows
• DBMS Mongo, MySQL, MS SQL
• Configuring Database Backup
• Knowledge and understanding of the basic principles of setting up networks and network routes

• A recent student or graduate of the field of Data Science, Computer Science, IT or related specialties.
• Basic knowledge of Python and libraries for working with data (NumPy, Pandas, Matplotlib).
• Understanding the basics of machine learning and experience working with scikit-learn.
• Basic knowledge of SQL and working with databases.
• Experience working with Jupyter Notebook.
• Understanding the basics of statistics, linear algebra and mathematical analysis.
• Understanding the principles of artificial intelligence and neural networks.
• Skills in using modern AI tools (ChatGPT, Claude, Gemini, etc.).
• Basic knowledge of C++, Java, HTML, CSS, JavaScript will be an advantage.
• The desire to develop in the direction of Data Science, Machine Learning and AI.

Data Engineering & Pipeline Development 

• Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners). 
• Create and optimise data flows for structured and unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake, and other modern engineering tools. 
• Develop and maintain specific data repositories, implementing enterprise‑level data models, and creating new models as needed. 
• Enable AI/ML readiness by ensuring data is well‑structured, versioned, traceable, and semantically aligned with enterprise data standards. 

Data Product & Architecture Partnership 

• Partner with data scientists, domain experts, and digital technology teams to translate business needs into high‑quality data products and engineering requirements. 
• Work closely with ontology/knowledge graph teams to implement semantic models and future‑proof data architectures. 

Quality, Compliance & Performance 

• Implement data quality and performance standards; define KPIs to measure accuracy, completeness, and consistency across the data assets. 
• Apply data versioning and lineage tracking for compliance, traceability, and audit readiness. 
• Follow software development best practices including code versioning, DevOps integration, and documentation. 

Cross‑Functional Collaboration 

• Engage with scientific, technical, and operations stakeholders to understand requirements, design data solutions, and drive adoption. 
• Support multiple concurrent projects, managing priorities, and delivering maximum business value across the network. 

What we expect:

• Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred. 
• 3+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment. 
• Proficiency with Python, R, SQL, and cloud-based architectures (AWS services, Snowflake, Databricks, Redshift). 
• Expertise in ETL and DWH.  
• Experience with NoSQL and graph databases. 
• English language proficiency of B2+ 
• Strong analytical, problem‑solving, and stakeholder‑management skills, with the ability to translate discussions into actionable requirements. 
• Ability to drive multiple exciting projects simultaneously with strong organizational skills and adaptability. 

Nice to have:

• Experience with regulated or standards‑driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards. 
• Familiarity with high‑dimensional data (e.g., imaging, sensor data, etc). 
• Experience with principles connecting to or feeding MLOps and model deployment workflows. 
• Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems. 
• Exposure to knowledge graph or ontology‑driven architectures.